Learning variational regularization models for image denoising by PDE constrained optimization
Dr. Carola-Bibiane Schönlieb (DAMTP, Cambridge)
Sala conferenze IMATI-CNR - Tuesday, June 16, 2015 h.16:00
Abstract. When assigned with the task of reconstructing an image from given data the first challenge one faces is the derivation of a truthful image and data model. Starting with a parameterized variational regularization model we propose a PDE constrained optimization approach to learn the image and data model from the data itself. We will discuss the analytic structure of this approach and consider its application to optimal parameter derivation for total variation de-noising with multiple noise distributions and for optimising higher-order total variation regularization for its application in photography.
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